Room 356

The AMS Short Course: Introduction to PyNIO and Related Python Tools for Geoscientific Data Analysis will be held on 10 January 2016 preceding the 96th AMS Annual Meeting in New Orleans, Louisiana.

Climate scientists are highly likely to encounter geoscientific datasets in many different and complex formats in the course of their research. These formats vary widely, making it a daunting task for researchers who need to quickly read and analyze their data. Python simplifies this task with libraries such as PyNIO, a unified and simple interface that helps take the fear out of reading and writing these formats. Python is a popular open source scripting language in the scientific community, given its ease-of-use, flexible design, well established scientific libraries, strong community support, large collection of third party software, and solid support for HPC environments.

The first goal of this course is to describe the various geoscientific data formats that climate scientists may encounter, including NetCDF, HDF, HDF-EOS, GRIB (1 and 2), and Shapefile, and then introduce PyNIO as a solution for handling these formats. A second goal is to introduce and demonstrate complementary Python tools and methods for performing computational analyses, producing publication quality 2D visualizations, and employing simple task parallelism to improve workflow performance. The third goal is to strongly encourage attendees to bring their own datasets of interest for use during the hands-on labs; the instructors will help attendees start writing custom Python scripts to read, analyze, and visualize their own data.

This course assumes attendees have a basic knowledge of using Python in a scientific environment, including the use of numeric and string variables, loops, conditional statements, and NumPy multi-dimensional arrays and functions. While new Python users can benefit from this course, they may be better suited in taking Dr. Johnny Lin’s course: “A Beginner’s Course to Using Python in Climate and Meteorology”.

Attendees are required to bring their own laptops with a power adapter, and with Python 2.7.x and NumPy installed. Instructions will be emailed before the course begins on how to install the required software.